Other works try to supplement the metric learning losses by introducing a regularization to optimize the direction of displacement of samples [1]} or maximize the spread-out of feature descriptors [2]}. Another work introduced second-order similarity [3]} inspired by graph matching and clustering as a regularizer for learning local feature descriptors.
